JP2007507774A - Method for simulating the interaction of a compound with an organism - Google Patents

Abstract

The method for determining the interaction of one or more active compounds with an organism is: A) Select one or more substances and their biochemical characteristics, lipophilicity, solubility, protein binding, molecular size, acid or base pKa Analyzing the characteristics of the system consisting of values, metabolic degradation rates, active transporter rate constants, B) compartments of the organism, a) mammals from lungs, peripheral organs, arterial or venous blood vessels, blood, b) Plants are selected from xylem, phloem flow, roots, leaves, stems, and C) the process of mass conservation and reaction rate with respect to the transport, degradation and action of substances from / to the compartment. D) In the case of intravenous, oral and subcutaneous administration, describe the transport process by mass conservation equation and reaction rate equation, and if applicable, describe the degradation process of the active compound in the organism. E) Selected in steps C) and D) By combining equations system, a system of differential equations obtained by the numerical calculation, the concentration of active compound in F) selected compartment - determining the time curve.

Description

  The present invention relates to a computer program for calculating the pharmacokinetic and pharmacodynamic behavior of a compound in an organism such as a mammal, insect or plant. The rate and extent of compound uptake, distribution, metabolism, and excretion in an organism are, for example, very important with respect to the action of pharmaceutically active compounds and crop protection agents in the body, or very important for toxicological risk assessment. is there. Computer programs such as physiological pharmacokinetic (PB-PK) modeling and pharmacodynamic (PD) modeling are suitable for describing the interaction processes of compounds with organisms. The present invention is based on a multi-level architecture in which individually encapsulated modules describing, for example, organisms, substances, types of administration or types of action are dynamically interconnected, making such a model very simple. To be generated. By linking these modules to an integrated model, a system of simultaneous differential equations (mass conservation equations) is generated and solved numerically by the solver. A particular advantage of the present invention is the high flexibility of modular computer programs, which allows a large number of complex physiological and biochemical scenarios to be generated in a very simple manner. The chemical, biological, physiological, and anatomical input parameters necessary to generate this system of equations may exist in one or more relational databases, or use a graphical user interface. It may be input via.

  For various organisms such as mammals [Non-patent document 1, Non-patent document 2, Non-patent document 3,], insects [Non-patent document 4], or plants [Non-patent document 5, Non-patent document 6] -PK and PD models are described in the literature. Such models have been successfully used to describe the behavior of compounds based on physiological and anatomical information and material-specific physicochemical and biochemical parameters. The current procedure consists in dividing the organism to be described into a plurality of physiological compartments. Frequently, individual organs such as liver, muscle, or lung correspond to these compartments. These compartments are characterized by physiological parameters such as blood flow rates and their water, fat, and protein content [7]. As detailed in the prior art above, to obtain a physiological model, these compartments are numerically linked to each other by a mass conservation equation to match the anatomical parameters of the organism.

R. Kawai, M .; Lemaire, J. et al. L. Steimer, A.M. Bruelisauer, W.M. Niederberger, M.C. Rowland: Physiologically based pharmacokinetic study on a cyclosporin derivative, SDZ IMM 125. J. et al. Pharmacokin. Biopharm. 22, 327-365 (1994); P. Paulin, F.M. P. Theil: Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J. et al. Pharm. Sci. 291, 129-156 (2002); P. Paulin, F.M. P. Theil: Prediction of Pharmacokinetics prior to in vivo studies. II. General physicologically based pharmacokinetic models of drug disposition, J. et al. Pharm. Sci. 91, 1358-1370 (2002) R. Greenwood, M.M. G. Ford, E.M. A. Peace, D.D. W. Salt: The Kinetics of Insectide Action. Part IV: The in vivo distribution of Pyrethroid Insectides durning Insect Poisoning. Plastic. Sci. 30, 97-121 (1990) N. M.M. Satchivi, E .; W. Stoller, L.M. M.M. Wax, D.D. P. Briskin: A nonlinear dynamic simulation model for xenobiotic transport and whole plant allocation following. Plastic. Biochem. Physiol. 68, 67-84 (2000); N. M.M. Satchivi, E .; W. Stoller, L.M. M.M. Wax, D.D. P. Briskin: A nonlinear dynamic simulation model for xenobiotic transport and whole plant allocation following. Plastic. Biochem. Physiol. 68, 67-84 (2000) R. Greenwood, M.M. G. Ford, E.M. A. Peace, D.D. W. Salt: The Kinetics of Insectide Action. Part IV: The in vivo distribution of Pyrethroid Insectides durning Insect Poisoning. Plastic. Sci. 30, 97-121 (1990)

  According to the prior art, this mathematical coupling is fixed, ie the differential equations are hard-wired to each other. This means that once a model is generated, it remains fixed with respect to the number of compartments and their physiological circuitry. The present invention dynamically interconnects a variable number of compartments and modules (eg, with respect to organisms, substances, or types of administration) according to individual parameters to automatically generate a corresponding physiological model and then numerical values It is based on the objective of developing a flexible way to solve the problem.

  This technical problem is solved by a novel modular programming method. This method is independent of each other, is closed on its own, and has various functions (eg, definition of substance properties or type of administration, or description of the physiological mechanism of the organism under consideration, etc.) It is based on a library of existing modules. It is only during the execution time of the program that these modules are dynamically combined to give an overall model. In this way, the method described herein allows for flexible handling of various complex scenarios for the first time without having to perform changes at the differential equation level.

  The idea of modular programming is known in principle in other fields of application (see for example US Pat. No. 5,930,154 or WO 00/65523). However, they have not been used so far for pharmacokinetic or pharmacodynamic problems.

A subject of the present invention that solves the above problems is a method for determining the interaction of one or more active compounds with an organism, comprising:
A) pKa in the case of selecting at least one substance and its physiological and / or biochemical characteristics, lipophilicity, solubility, protein binding, molecular size (expressed in molecular weight or molar volume), acid or base Analyzing features from a series consisting of values, metabolic degradation rates, and active transporter rate constants;
B) the anatomical and physiological compartments of the organism, at least in the case of mammals from the lungs, peripheral organs, preferably blood vessels that are arteries and / or veins, or blood, b) plant In some cases, selecting from xylem and phloem flows and roots and / or leaves and / or stems;
C) a computer program for said compartment by means of a mass conservation equation and a reaction rate equation for describing the process of transporting the substance from the compartment and the process of transporting the substance to the compartment, the decomposition process and the action process; The steps described in
D) In the case of intravenous, oral, or subcutaneous administration, describing the transport process in a computer program with mass conservation equations and reaction rate equations and, where applicable, describing the degradation process of the active compound in the organism When,
E) combining the system of equations selected in steps C) and D) and numerically calculating the resulting system of simultaneous differential equations;
F) determining a concentration-time curve of the active compound in the selected compartment.

  Said organism preferably represents a mammal from the group consisting of humans, monkeys, dogs, pigs, rats or mice, or insects, in particular caterpillars, or plants.

  According to a preferred embodiment, the active compound is administered by intravenous, oral, application, subcutaneous, peritoneal route, inhalation route via the nose or lung, or intracerebral blood route (in the case of mammals) or blood It can be administered intralymphatically, by application, or by oral route by feeding or forced feeding (in the case of worms), or in the form of a spray mixture by ingestion of leaves or through roots (in the case of plants).

  Preferred is a method characterized in that the physiological parameter describing the organism is a time-dependent parameter.

  In the preferred embodiment of the method, some of the parameters are obtained from a database linked to a computer system.

  In a preferred embodiment, physiological and anatomical parameters can be varied using random numbers (population kinetics) within predetermined statistical variability.

  Modules with different functions and chemical or biological characteristics of the substance, or physiological or anatomical information about the organism, or information on the mode of administration or type of action Particularly preferred is a method in which a hierarchical model architecture consisting of at least two modules is used for a computer program.

  A particularly preferred variant of this method is that multiple models of organisms can be combined into an integrated model, especially to simulate drug-drug interactions, maternal-fetal models, or insect-plant coupled models. It is characterized by.

The heart of the present invention is the realization of the concept of modular simulation by the dynamic generation of differential equation (DE) systems and their application to pharmacokinetic and pharmacodynamic problems. The simulation kernel for this purpose consists of a library of independent modules with the following functions:
● Definition of physicochemical properties of compounds (eg lipophilicity, affinity with plasma proteins, molecular weight)
● Anatomical and physiological description of the organism (mammals, insects, plants) ● Description of the individual compartments (organs) that make up the organism ● Description of the type of administration of the compound (eg, in mammals, intravenous or oral) Administered by subcutaneous, application, or inhalation route, in the case of worms, by oral or application route, in the case of plants, administered as a spray mixture and taken in the leaves or via the roots Ingested)
● Definition of time loop for numerical integration ● Numerical integration of differential equations (eg, by Euler or Runge-Kutta methods)
● Save simulation results (for example, to a file or database)

  These individual modules are dynamically interconnected in a hierarchical and predetermined manner during the execution time of the program by a computer system such as a commercially available PC. Thereafter, a system of simultaneous DEQs (mass conservation equations) is automatically generated and solved by numerical integration. As a result, the model yields a concentration-time curve for each compound for the various compartments of the organism, and this curve can then be used for other purposes, such as calculating pharmacological effects.

  The minimal model upon which the present invention is based consists of at least one substance module, a dosing module, a biological module, a DEQ builder, and a numerical solver for the DEQ system, optionally further including, for example, a module for pharmacological action Consists of modules.

  The material module contains physicochemical and / or biochemical information about the material whose behavior is to be simulated. For example, this information includes lipophilicity, eg solubility in water or intestinal fluid, eg protein binding of plasma proteins, molecular size (expressed in molecular weight or molar volume), pKa value in the case of acids or bases, rate of metabolic degradation , And kinetic constants of active transporters, and the like. These parameters can generally be determined by in vitro experiments, or calculated directly from the structure in individual cases, for example in the case of lipophilicity, for example by known prediction models using QSAR, HQSAR or neuronal networks. can do.

  The biology module contains physiological and anatomical information that characterizes the organism that is the subject of the interaction of matter. Biological specific information is, inter alia, the volume of individual compartments, the volumetric flow rate of the blood stream, and the content of water, fat and protein (in the case of mammals and insects), or in the case of plants, the xylem and The phloem transport rate and the volume of symplasts, apoplasts and vacuoles. These physiological parameters may be constant in time, or may be a function of time so that, for example, growth processes or other physiological changes over time can be taken into account. These physiological and anatomical parameters are published in the literature for a number of related organisms.

  The dosing module contains information about the location and time course of administration of the substance. For mammals, conventional forms of administration of pharmaceutically active compounds include intravenous administration in the form of boluses or infusions, oral administration in the form of solutions, capsules or tablets, subcutaneous administration, intraperitoneal administration, Administration by application absorbed through the skin or mucous membrane, and nasal or inhalation administration. In insects, compound absorption is exclusively absorption through the integument after oral or topical contact. In plants, the absorption of substances takes place via the roots or leaves.

  The DEQ builder automatically generates a differential equation system based on the mass balance in the living body. The solver processes the numerical integration of the DEQ system and results in a concentration / time transition of the compound in each compartment of the organism. These data can be further utilized, for example, to describe the pharmacological effects at the target enzyme.

  Linking modules in a dynamic manner requires hierarchical management of the variables contained in the modules because various variables from separate modules may be interdependent. That is, for example, to calculate the equilibrium partition coefficient between the plasma and peripheral compartments of the mammalian body, the substance-specific physicochemical parameters (from the substance module) are combined with physiological information (from the biological module). used. Similarly, there are dependencies between individual compartments of the same organism. For example, blood flow in the lungs is a result of the sum of the blood flow velocities of all other organs because the blood circulation in the mammalian body is a closed system. Such dependencies require an overall hierarchical data structure so that they can be automatically recognized and taken into account. Such a hierarchical data structure is part of the present invention. For this purpose, modules are managed from a database of objects. The object database can be expanded dynamically as desired, ensuring maximum functionality. Multiple modules having the same or similar functions are also acceptable. Data structures for mathematical description of the integration model are encapsulated in individual modules. A feature of this idea is the hierarchical combination of data between modules, which is then managed by a separate database, similar to an object module. This allows model definitive access to data from other modules. The modules of the simulation kernel must be generated by the master program and combined in the desired order.

  In order to manage the various input parameters, a connection with a database of compound and biological parameters is first realized. In addition (or as the only alternative), a graphical user interface (GUI) ensures that all relevant model parameters are (a) visible to the user and (b) editable by the user. To do. Changes to individual data in a module result in a dependency check in the remaining modules via a predetermined message according to the data hierarchy. In a model generator, checking data dependencies is an essential component for ensuring that the model is correct at any given time. Resulting curves and, optionally, typical pharmacokinetic parameters derived from those curves (eg, area under the curve, maximum concentration, time of maximum concentration, half-life, etc.) or pharmacodynamic parameters (eg, The intensity and duration of action) is displayed by the GUI.

  A particular advantage of the present invention is the flexibility to generate a variety of complex models in a simple manner. The following example is intended to explain this fact with reference to the drawings. They show various related scenarios with different complexity. However, these examples should not be construed as limiting the applicability of the present invention.

  In this section, preferred embodiments are described for the organisms to be described (mammals, worms and plants), and their incorporation into dynamic simulation models is then shown with respect to the examples.

Various Biological Module Examples FIG. 1 shows a very simplified four-compartment model for a mammalian body. Here, the mammalian body is composed solely of venous and arterial blood pools, lungs, and additional peripheral compartments through which substances are removed by metabolism. A substance (blood concentration C ⁱⁿ _org ) is supplied to the peripheral compartment by the flow of arterial blood (blood flow velocity Q _org ). Following the interaction in this compartment, venous blood (substance concentration C ^out _org ) flows into the lungs and the circulation of the blood is complete (Q _lungs = Q _org ).

  In a preferred embodiment of the present invention, the mammalian model shown in FIG. 2 is used as a biological module. Again, the mammalian body is divided into arterial and venous blood pools, lung compartments and the following peripheral organs: liver, kidney, muscle, bone, fat, brain, stomach, small intestine, large intestine, pancreas, spleen, gallbladder , And is composed of testis. This embodiment is hereinafter referred to as “whole body model”. Each organ of this whole body model is then divided into multiple sub-compartments, each representing vascular space (consisting of plasma and red blood cells), interstitial space, and intracellular space ( FIG. 3). Plasma and interstitial space are treated as being in equilibrium. The transport of substances between the interstitial space and the intracellular space is modeled by a passive diffusion process according to the first-order kinetics or the saturable Michaelis-Menten kinetics Described as an active transport process. Similarly, one or more Michaelis-Menten terms are present in each of the intracellular subcompartments that take into account the metabolic degradation of the substance.

  The resulting system of simultaneous differential equations has the following form. That is, three differential equations for sub-compartments "plasma" (pl), "red blood cells" or "blood cells" (bc), and "intracellular" (cell) exist for each peripheral organ subscript "org" To do. The whole body model includes the following peripheral organs: lung, stomach, small intestine, large intestine, pancreas, spleen, liver, kidney, brain, heart, muscle, bone, skin, fat, and testis. The following mass conservation equations are given for the plasma and interstitial space (int) of each organ:

意味：コンパートメント間の流れの項

Meaning: Flow between compartments

意味：拡散による赤血球への質量輸送

Meaning: Mass transport to red blood cells by diffusion

意味：拡散による細胞内への質量輸送

Meaning: Mass transport into cells by diffusion

意味：細胞内への能動輸送項（ｉｎｆｌｕｘ）

Meaning: Intracellular active transport term (influx)

意味：細胞内からの能動輸送項（ｅｆｆｌｕｘ）

Meaning: Active transport term (efflux) from inside the cell

意味：コンパートメント間の流れの項

The following mass conservation equation is provided for red blood cells:

Meaning: Flow between compartments

意味：拡散による赤血球への質量輸送

Meaning: Mass transport to red blood cells by diffusion

意味：拡散による細胞内への質量輸送 The interior of the cell is described by the following mass conservation equation.

Meaning: Mass transport into cells by diffusion

意味：細胞内への能動輸送項（ｉｎｆｌｕｘ）

Meaning: Intracellular active transport term (influx)

意味：細胞内からの能動輸送項（ｅｆｆｌｕｘ）

Meaning: Active transport term (efflux) from inside the cell

意味：代謝による分解（ｅｎｚｙｍｅ １）

Meaning: Metabolic degradation (enzyme 1)

意味：代謝による分解（ｅｎｚｙｍｅ ２）

Meaning: Metabolic degradation (enzyme 2)

意味：一次分解（例えば、肝臓または腎臓における）

Meaning: primary degradation (eg in liver or kidney)

意味：血液の流れが、他の器官と比べて反対である！

The lung (org = lng) is described in the same way, but the inward flow comes from the venous blood pool (ven), not from the arterial blood pool (art).

Meaning: Blood flow is opposite to other organs!

± ...
Meaning: Like other organs

意味：血液の流れが、他の器官と比べて反対である！

Meaning: Blood flow is opposite to other organs!

± ...
Meaning: Like other organs

意味：血漿（−）と赤血球（＋）との間の拡散による質量輸送 The concentrations that flow out of each organ are combined to form a venous blood pool. The resulting plasma concentration is the result of a blood flow weighted average of individual organ concentrations.

Meaning: Mass transport by diffusion between plasma (-) and red blood cells (+)

意味：血漿クリアランス（該当する場合）

Meaning: Plasma clearance (if applicable)

意味：静脈血プールへの投与についての入力関数

Meaning: Input function for administration to venous blood pool

X = {1-HCT (in the case of plasma)
{HCT (for red blood cells)

意味：血漿（−）と赤血球（＋）との間の拡散による質量輸送 Finally, the arterial blood pool is described by the following mass conservation equation:

Meaning: Mass transport by diffusion between plasma (-) and red blood cells (+)

X = {1-HCT (in the case of plasma)
{HCT (for red blood cells)

In this system of equations,
f _x ^org means the volume ratio of organ blood vessels (x = vas), stroma (x = int), or intracellular space (x = cell),
V ^org means the volume of the organ,
HCT means red blood cell concentration (= volume ratio of red blood cells in whole blood)
Q _x ^org means blood flow velocity of plasma (x = pl) or blood cells (x = bc) in the organ,
PA _bc ^org means the product of red blood cell permeability and surface area,
PA ^org means the product of organ permeability and surface area,
K _pl means the equilibrium partition coefficient of the substance between plasma and water,
K _bc means the equilibrium partition coefficient of the substance between red blood cells and plasma,
K ^org means the equilibrium partition coefficient of the substance between the organ and plasma,
V _max and K _m mean the Michaelis-Menten constant for active transport and metabolism, respectively.

  In addition to intravenous administration as described herein, or in addition to intravenous administration as described herein, it is also possible to simulate ingestion through the gastrointestinal mucosa following oral administration. Is possible. The solution of this system of equations yields a concentration-time relationship for all compartments present in the model.

  To describe the pharmacological action, the concentration-time relationship of the compartment containing the biological target of the active compound can be linked to the pharmacodynamic effect. An example of a typical effect function is as follows.

Ｅｆｆｅｃｔ＝薬理的作用のパラメータ（時間に依存する）
Ｅ_０＝薬理的作用のパラメータのベース値
Ｅ_ｍａｘ＝最大の薬理的作用
ＥＣ_５０＝最大の効果の５０％が得られる濃度
Ｃ_ｘ＝作用場所における濃度（時間に依存する）
γ＝形態パラメータ ● Hyperbolic or Sigmoidal Emax models:

Effect = parameter of pharmacological action (dependent on time)
E ₀ = Base value of parameters of pharmacological action E _max = maximum pharmacological action EC ₅₀ = concentration at which 50% of maximum effect is obtained C _x = concentration at the place of action (time dependent)
γ = form parameter

または対数線形モデル：

Ｅｆｆｅｃｔ＝薬理的作用のパラメータ（時間に依存する）
Ｅ_０＝薬理的作用のパラメータのベース値
β＝濃度の関数としての効果の増大のパラメータ
Ｃ_ｘ＝作用場所における濃度（時間に依存する）
γ＝形態パラメータ ● Power function:

Or a log-linear model:

Effect = parameter of pharmacological action (dependent on time)
E ₀ = base value of parameters of pharmacological action β = parameter of increasing effect as a function of concentration C _x = concentration at the place of action (time-dependent)
γ = form parameter

● Effective compound interaction models, eg partial or total antagonism ● Combination of the above models to help describe multiple action centers or receptor-transducer interactions

  In a further preferred embodiment of the invention, the biological module represents the anatomy of worms, in particular caterpillar (FIG. 4). The body of the caterpillar is in the following compartments: hemolymph as the central compartment, outer skin as the peripheral compartment, muscles, fat body, nervous system and gastrointestinal wall, and through which the substance is exchanged with the surroundings It consists of the outer skin surface and the digestive tract compartment. Secondly, the movement of mass between compartments can take the form of passive movement by diffusion or active processes with the aid of transporters. The resulting differential equation system is described in the appendix of the unpublished German patent application (Application No. 10256315.2).

  The plant model of FIG. 5 constitutes a further preferred embodiment. According to the typical physiological mechanisms of plants, this model describes the roots, stems and leaves of plants. Each of these compartments is then made up of three sub-compartments representing a vacuole, symplast, and apoplast. These sub-compartments are separated from each other by a membrane. Similar to the organisms described above, these membranes are again permeable by passive diffusion or active transport. In addition, the subcompartments are characterized by different pH values that greatly influence the partitioning behavior of acids and bases in particular. Plants have two translocation pathways between compartments. While the xylem flux in the apoplast flows from the root to the leaves, the phloem flux moves the material from the leaves to the roots in the symplast.

Model structure example ● The simplest model structure (Fig. 6)
FIG. 6 shows the simplest model structure. The entire model is composed of modules such as “compound”, “administration”, “organism”, “DEQ builder”, and “integrator”. “Action modules” can optionally be taken into account. FIG. 7 conceptually illustrates links to one or more databases that can include, for example, compound parameters or physiological and anatomical information of the organism.

● Multiple administration (Figure 8)
Repeated administration of active compounds to one and the same organism is a realistic scenario for a large number of medicinal compounds that must be taken at regular intervals over a long period of time, for example anti-infective active compounds. This model is an example of how long-term effects, such as the accumulation of active compounds in individual organisms, can be simulated.

● Enterohepatic circulation (Figure 9)
A series of compounds administered to mammals enters the enterohepatic circulation. In such cases, some of the administered compound is hidden in the bile in the liver without change and accumulates in the gallbladder (represented by organ N in FIG. 9). The stimulation of the compound triggers the gallbladder to shrink, for example by eating food, and the contents, that is, the bile, is released into the duodenum. This bile contains a particularly active compound. This compound can then be absorbed by the gastrointestinal tract and enter the systemic circulation again. In the model outlined in FIG. 8, this process can be described simply as the administration of a portion of the original compound to a new intestine (dosing 2, characterized by a time difference).

● Drug-drug interaction (Figure 10)
The interaction between multiple compounds (in this example, two compounds) between each other is extremely important. According to the model, this case can be seen as follows. Organisms 1 and 2 are defined by the same physiological parameters. This is because organisms 1 and 2 represent one and the same organism. Two different compounds are administered separately. For these two compounds, the route, timing and duration of administration may be different. The actual interaction of the two compounds is defined in an action module that links the organs N of organisms 1 and 2. For example, the interaction can be competitive inhibition and any other biochemical interaction that can occur.

● Mother-fetus model (Figure 11)
The maternal-fetal model of FIG. 11 is an example in which two separate but connected organisms are modeled simultaneously. Living organism 1 represents a mother, while living organism 2 represents a fetus. The blood circulation of the fetus is connected to the mother's blood circulation via the placenta (in this case represented by organ N of organism 1). In this example, physiological parameters, particularly fetal organ volume and blood flow velocity, can be a function of time so that the fetal pregnancy age and growth of the mother can be accurately taken into account. Is very important.

● Plant / Insect Combination Model (Figure 11)
Like the maternal-fetal model, the plant / worm model is a combination of two living organisms, but in this case the biological mechanisms of the organisms are different. Describes a plant in which organism 1 absorbs a compound, such as an insecticide, administered via a leaf crust or root. After distribution throughout the organism, the compound is also available in the remaining compartments. By feeding from the leaves that make up this particular compartment of the plant, it is possible to feed the compound (organism 2), in which the compound is distributed and eventually, for example, nerves of the tubeworm Insecticidal action is achieved in target organs such as the system.

Further combinations of the above examples Further combinations of the above examples are also very important. For example, multiple administrations of two or more interacting substances in a maternal-fetal model can be used for early assessment of toxicological risk to the fetus.

● Simulation results for the whole body model of mammals The following text provides an example of the steps required to perform a simulation. First, compound dependent properties must be determined. Compound X having the compound dependent parameters of Table 1 below is used as an example.

Now, the organ partition coefficient is expressed in the published equation [M. Harter, J .; Keldenich, W.M. Schmitt: Estimation of physicochemical and ADME parameters, Ch. 26 in: Combinatorial Chemistry-A Practical Handbook, Part IV. Eds. K. C. Nicolau et al. , Wiley VCH, Weinheim, with the aid of 2002, can be calculated from the lipophilic (MA = 10 ^ LogMA) and unbound plasma portion _{(f u).} In this example, the following values are provided for the organ partition coefficients: Table 2 below is the partition coefficient [8] resulting from the values in Table 1.

In addition, physiological parameters such as organ volume, organ blood flow velocity and composition must be known in terms of blood vessels, stroma and cell space. These parameters are also described in the literature. For example, the values listed in Tables 3-5 can be found for mouse, rat, dog, and human species.
Table 3 below shows the volume (document data) of mouse, rat, dog, and human organs.

Table 4 below shows blood flow velocities (literature data) of mouse, rat, dog, and human organs.

Table 5 below shows the composition (literature data) of mammalian organs.

The following text shows the simulation results realized by the whole body model as shown in FIG. As the simplest boundary case, it was assumed that transport to the organ was limited to blood flow (ie PA ^org → infinity).

  FIG. 12 shows the concentration of each organ obtained after intravenous administration of Compound X at 1 mg / kg in the form of a bolus for rats.

  FIG. 13 shows simulated concentrations-time curves in plasma after intravenous administration of Compound X at 1 mg / kg in bolus form for four different species.

  FIG. 14 shows concentration-time curves after various intravenous doses of Compound X in bolus form at 1 mg / kg over 3 days for various human organs (dosing schedule: 6h-6h-12h). , Calculated according to the model of FIG.

  FIG. 15 shows the concentration-time curve in plasma after oral administration of Compound X at 1 mg / kg for humans, calculated according to the model of FIG. 9 with no enterohepatic circulation and with enterohepatic circulation ( It is assumed that clearance in bile is equivalent to 25% of total clearance).

FIG. 2 is a schematic diagram of a four-compartment mammalian model. 1 is a schematic view of a physiological model of the entire mammalian body. It is the schematic of the organ in a whole body model. 1 is a schematic diagram of a physiological model for a caterpillar. 1 is a schematic view of a physiological plant model. FIG. Schematic diagram of the simplest model scenario, where the model consists of “compound”, “administration”, “biology”, “DEQ builder”, and “integrator” modules, optionally “action module” Can be taken into account. FIG. 2 is a schematic diagram of a link to a simulation model database and a graphical user interface. FIG. 6 is a schematic diagram of a model for multiple administrations. It is the schematic of the model about the substance which enters into the enterohepatic circulation. It is the schematic of the model for describing the interaction between two substances. FIG. 3 is a schematic diagram of a model for two interacting organisms. 2 is a concentration-time curve of substances in various organs of rats. Figure 2 is a concentration-time curve of substances in the plasma of various mammals. 2 is a concentration-time curve of substances in various human organs. Figure 2 is a concentration-time curve of a substance in human plasma after oral administration.

Claims (8)

A method for determining the interaction of one or more active compounds with an organism, comprising:
A) pKa in the case of selecting at least one substance and its physiological and / or biochemical characteristics, lipophilicity, solubility, protein binding, molecular size (expressed in molecular weight or molar volume), acid or base Analyzing features from a series consisting of values, metabolic degradation rates, and active transporter rate constants;
B) the anatomical and physiological compartments of the organism, at least in the case of mammals from the lungs, peripheral organs, preferably blood vessels that are arteries and / or veins, or blood, b) plant In some cases, selecting from xylem and phloem flows and roots and / or leaves and / or stems;
C) a computer program for said compartment by means of a mass conservation equation and a reaction rate equation for describing the process of transporting the substance from the compartment and the process of transporting the substance to the compartment, the decomposition process and the action process; The steps described in
D) for intravenous, oral or subcutaneous administration, describing the transport process in a computer program with a mass conservation equation and a reaction rate equation, and where applicable, describing the degradation process of the active compound in the organism; ,
E) combining the system of equations selected in steps C) and D) and numerically calculating the resulting system of simultaneous differential equations;
F) determining a concentration-time curve of the active compound in the selected compartment.   The method according to claim 1, wherein the organism represents a mammal consisting of a group consisting of humans, monkeys, dogs, pigs, rats, or mice, or an insect or plant containing a tuberworm.   The active compound is administered by intravenous, oral, application, subcutaneous, abdominal route, inhalation route through the nose or lung, or intracranial route in the case of mammals, or in the hemolymph in the case of worms, Application according to claim 1 or 2, characterized by being administered by oral route by application or feeding or gavage, or in the case of plants in the form of a spray mixture by ingestion of leaves or ingestion via roots. Method.   The method according to claim 1, wherein the physiological parameter describing the organism is a time-dependent parameter.   5. A method as claimed in any preceding claim, wherein some of the parameters are obtained from a database linked to the computer system.   The method according to claim 1, wherein the physiological and anatomical parameters are varied by random numbers (population dynamics) within a predetermined statistical variation.   Modules with different functions and chemical or biological characteristics of the substance, or physiological or anatomical information about the organism, or information on the mode of administration or type of action 7. A method according to any one of the preceding claims, wherein a hierarchical model architecture consisting of at least two modules is used for the computer program. A plurality of models for the organism are combined into an integrated model, particularly for simulating drug-drug interactions, maternal-fetal models, or insect-plant coupled models. The method according to any one of 7 above.

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